Here we use DFTS2 to conduct what we believe are the most comprehensive experiments on packet loss resilient collaborative image classification. Experiments were run on the same 882 images used in [1]}, [2]}, which are a subset of 10 classes of the ImageNet [3]} test set. Pre-trainedhttps://github.com/qubvel/classification_models DNNs, namely ResNet-18 [4]}, ResNet-34 [4]}, DenseNet-121 [6]}, and EfficientNet-B0 [7]}, were each split at the output of an early layer with a single connection to downstream layers.
This simulation scenario corresponds to a mobile/edge device having very limited computational capabilities,
hence the split at an early layer. Of course, DFTS2 also supports split layers deeper in the DNN. Layer add_1 for ResNet-18 and layer add_3 for ResNet-34 correspond to the output of the shortcut connection and element-wise addition applied to the final output of the conv2_x layers [4]}. This means that a single deep feature tensor
\(\mathcal {X} \in \mathbb {R}^{56 \times 56 \times 64}\) 
is to be transmitted to the cloud.
For DenseNet-121, pool2_conv is the output of convolution layer in the first transition layer block, as denoted in [6]}. In this case, \(\mathcal {X} \in \mathbb {R}^{56 \times 56 \times 128}\) . In the case of EfficientNet-B0 block2b_add tensors, \(\mathcal {X} \in \mathbb {R}^{56 \times 56 \times 24}\) . With \(r_p = 8\)  rows of features/packet, we end up with 7 packets per tensor channel in the case of ResNet-18 add_1, ResNet-34 add_3, DenseNet-121 pool2_conv, and EfficientNet-B0 block2b_add. When ResNet-18 is split at the output of the add_3 layer, the transmitted feature tensor has dimensions \(28 \times 28 \times 64\) . With \(r_p = 4\)  rows of features/packet, we end up with 7 packets per tensor channel.
